Principles of Adaptive Filters and Self-learning Systems

• Main Author: Zaknich, Anthony (Author)
• Corporate Author: SpringerLink (Online service)
• Other Authors: Grimble, Michael J. (Editor), Johnson, Michael A. (Editor)
• Format: Electronic eBook
• Language: English
• Published: London : Springer London, 2005.
• Series: Advanced Textbooks in Control and Signal Processing,
• Subjects: Engineering.; Artificial intelligence.; Mechanical engineering.; Control engineering.; Robotics.; Mechatronics.; Electrical engineering.; Signal, Image and Speech Processing.; Artificial Intelligence (incl. Robotics).; Control, Robotics, Mechatronics.; Electrical Engineering.; Mechanical Engineering.; Engineering, general.

Kalman and Wiener Filters, Neural Networks, Genetic Algorithms and Fuzzy Logic Systems Together in One Text Book How can a signal be processed for which there are few or no a priori data? Professor Zaknich provides an ideal textbook for one-semester introductory graduate or senior undergraduate cour...

Table of Contents:

• Adaptive Filtering
• Linear Systems and Stochastic Processes
• Modelling
• Optimisation and Least Squares Estimation
• Parametric Signal and System Modelling
• Classical Filters and Spectral Analysis
• Optimum Wiener Filter
• Optimum Kalman Filter
• Power Spectral Density Analysis
• Adaptive Filter Theory
• Adaptive Finite Impulse Response Filters
• Frequency Domain Adaptive Filters
• Adaptive Volterra Filters
• Adaptive Control Systems
• Nonclassical Adaptive Systems
• to Neural Networks
• to Fuzzy Logic Systems
• to Genetic Algorithms
• Adaptive Filter Application
• Applications of Adaptive Signal Processing
• Generic Adaptive Filter Structures.